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1.  Efficient prediction of human protein-protein interactions at a global scale 
BMC Bioinformatics  2014;15(1):383.
Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.
On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.
The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users.
PMCID: PMC4272565  PMID: 25492630
Protein-protein interactions; Computational prediction; Human proteome; Massively parallel computing; Personalized medicine; Interactome; Network analysis
2.  Quantitative Genome-Wide Genetic Interaction Screens Reveal Global Epistatic Relationships of Protein Complexes in Escherichia coli 
PLoS Genetics  2014;10(2):e1004120.
Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.
Author Summary
Genome-wide genetic interaction (GI) screens have been performed in yeast, but no analogous large-scale studies have yet been reported for bacteria. Here, we have used E. coli synthetic genetic array (eSGA) technology developed by our group to quantitatively map GIs to reveal epistatic dependencies and functional cross-talk among ∼600,000 digenic mutant combinations. By combining this epistasis information with functional modules derived by our group's earlier efforts from proteomic and genomic context (GC)-based methods, we identify several unexpected pathway-level dependencies, functional links between protein complexes, and biological roles of uncharacterized bacterial gene products. As part of the study, two of our pathway predictions from GI screens were validated experimentally, where we confirmed the role of these new components in iron-sulphur biogenesis and ribosome integrity. We also extrapolated the epistatic connectivity diagram of E. coli to 233 distantly related γ-proteobacterial species lacking GI information, and identified co-conserved genes and functional modules important for bacterial pathogenesis. Overall, this study describes the first genome-scale map of GIs in gram-negative bacterium, and through integrative analysis with previously derived protein-protein and GC-based interaction networks presents a number of novel insights into the architecture of bacterial pathways that could not have been discerned through either network alone.
PMCID: PMC3930520  PMID: 24586182
3.  Phosphatase Complex Pph3/Psy2 Is Involved in Regulation of Efficient Non-Homologous End-Joining Pathway in the Yeast Saccharomyces cerevisiae 
PLoS ONE  2014;9(1):e87248.
One of the main mechanisms for double stranded DNA break (DSB) repair is through the non-homologous end-joining (NHEJ) pathway. Using plasmid and chromosomal repair assays, we showed that deletion mutant strains for interacting proteins Pph3p and Psy2p had reduced efficiencies in NHEJ. We further observed that this activity of Pph3p and Psy2p appeared linked to cell cycle Rad53p and Chk1p checkpoint proteins. Pph3/Psy2 is a phosphatase complex, which regulates recovery from the Rad53p DNA damage checkpoint. Overexpression of Chk1p checkpoint protein in a parallel pathway to Rad53p compensated for the deletion of PPH3 or PSY2 in a chromosomal repair assay. Double mutant strains Δpph3/Δchk1 and Δpsy2/Δchk1 showed additional reductions in the efficiency of plasmid repair, compared to both single deletions which is in agreement with the activity of Pph3p and Psy2p in a parallel pathway to Chk1p. Genetic interaction analyses also supported a role for Pph3p and Psy2p in DNA damage repair, the NHEJ pathway, as well as cell cycle progression. Collectively, we report that the activity of Pph3p and Psy2p further connects NHEJ repair to cell cycle progression.
PMCID: PMC3909046  PMID: 24498054
4.  The Antifungal Eugenol Perturbs Dual Aromatic and Branched-Chain Amino Acid Permeases in the Cytoplasmic Membrane of Yeast 
PLoS ONE  2013;8(10):e76028.
Eugenol is an aromatic component of clove oil that has therapeutic potential as an antifungal drug, although its mode of action and precise cellular target(s) remain ambiguous. To address this knowledge gap, a chemical-genetic profile analysis of eugenol was done using ∼4700 haploid Saccharomyces cerevisiae gene deletion mutants to reveal 21 deletion mutants with the greatest degree of susceptibility. Cellular roles of deleted genes in the most susceptible mutants indicate that the main targets for eugenol include pathways involved in biosynthesis and transport of aromatic and branched-chain amino acids. Follow-up analyses showed inhibitory effects of eugenol on amino acid permeases in the yeast cytoplasmic membrane. Furthermore, phenotypic suppression analysis revealed that eugenol interferes with two permeases, Tat1p and Gap1p, which are both involved in dual transport of aromatic and branched-chain amino acids through the yeast cytoplasmic membrane. Perturbation of cytoplasmic permeases represents a novel antifungal target and may explain previous observations that exposure to eugenol results in leakage of cell contents. Eugenol exposure may also contribute to amino acid starvation and thus holds promise as an anticancer therapeutic drug. Finally, this study provides further evidence of the usefulness of the yeast Gene Deletion Array approach in uncovering the mode of action of natural health products.
PMCID: PMC3799837  PMID: 24204588
5.  Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps 
Scientific Reports  2012;2:239.
A goal of the post-genomics era has been to elucidate a detailed global map of protein-protein interactions (PPIs) within a cell. Here, we show that the presence of co-occurring short polypeptide sequences between interacting protein partners appears to be conserved across different organisms. We present an algorithm to automatically generate PPI prediction method parameters for various organisms and illustrate that global PPIs can be predicted from previously reported PPIs within the same or a different organism using protein primary sequences. The PPI prediction code is further accelerated through the use of parallel multi-core programming, which improves its usability for large scale or proteome-wide PPI prediction. We predict and analyze hundreds of novel human PPIs, experimentally confirm protein functions and importantly predict the first genome-wide PPI maps for S. pombe (∼9,000 PPIs) and C. elegans (∼37,500 PPIs).
PMCID: PMC3269044  PMID: 22355752
6.  Genetic Interaction Maps in Escherichia coli Reveal Functional Crosstalk among Cell Envelope Biogenesis Pathways 
PLoS Genetics  2011;7(11):e1002377.
As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.
Author Summary
Proper assembly of the cell envelope is essential for bacterial growth, environmental adaptation, and drug resistance. Yet, while the biological roles of the many genes and pathways involved in biosynthesis of the cell envelope have been studied extensively in isolation, how the myriad components intersect functionally to maintain envelope integrity under different growth conditions has not been explored systematically. Genome-scale genetic interaction screens have increasingly been performed to great impact in yeast; no analogous comprehensive studies have yet been reported for bacteria despite their prominence in human health and disease. We addressed this by using a synthetic genetic array technology to generate quantitative maps of genetic interactions encompassing virtually all the components of the cell envelope biosynthetic machinery of the classic model bacterium E. coli in two common laboratory growth conditions (rich and minimal medium). From the resulting networks of high-confidence genetic interactions, we identify condition-specific functional dependencies underlying envelope assembly and global remodeling of genetic backup mechanisms that ensure envelope integrity under environmental challenge.
PMCID: PMC3219608  PMID: 22125496
7.  Binding Site Prediction for Protein-Protein Interactions and Novel Motif Discovery using Re-occurring Polypeptide Sequences 
BMC Bioinformatics  2011;12:225.
While there are many methods for predicting protein-protein interaction, very few can determine the specific site of interaction on each protein. Characterization of the specific sequence regions mediating interaction (binding sites) is crucial for an understanding of cellular pathways. Experimental methods often report false binding sites due to experimental limitations, while computational methods tend to require data which is not available at the proteome-scale. Here we present PIPE-Sites, a novel method of protein specific binding site prediction based on pairs of re-occurring polypeptide sequences, which have been previously shown to accurately predict protein-protein interactions. PIPE-Sites operates at high specificity and requires only the sequences of query proteins and a database of known binary interactions with no binding site data, making it applicable to binding site prediction at the proteome-scale.
PIPE-Sites was evaluated using a dataset of 265 yeast and 423 human interacting proteins pairs with experimentally-determined binding sites. We found that PIPE-Sites predictions were closer to the confirmed binding site than those of two existing binding site prediction methods based on domain-domain interactions, when applied to the same dataset. Finally, we applied PIPE-Sites to two datasets of 2347 yeast and 14,438 human novel interacting protein pairs predicted to interact with high confidence. An analysis of the predicted interaction sites revealed a number of protein subsequences which are highly re-occurring in binding sites and which may represent novel binding motifs.
PIPE-Sites is an accurate method for predicting protein binding sites and is applicable to the proteome-scale. Thus, PIPE-Sites could be useful for exhaustive analysis of protein binding patterns in whole proteomes as well as discovery of novel binding motifs. PIPE-Sites is available online at
PMCID: PMC3120708  PMID: 21635751
8.  Ribosome-Dependent ATPase Interacts with Conserved Membrane Protein in Escherichia coli to Modulate Protein Synthesis and Oxidative Phosphorylation 
PLoS ONE  2011;6(4):e18510.
Elongation factor RbbA is required for ATP-dependent deacyl-tRNA release presumably after each peptide bond formation; however, there is no information about the cellular role. Proteomic analysis in Escherichia coli revealed that RbbA reciprocally co-purified with a conserved inner membrane protein of unknown function, YhjD. Both proteins are also physically associated with the 30S ribosome and with members of the lipopolysaccharide transport machinery. Genome-wide genetic screens of rbbA and yhjD deletion mutants revealed aggravating genetic interactions with mutants deficient in the electron transport chain. Cells lacking both rbbA and yhjD exhibited reduced cell division, respiration and global protein synthesis as well as increased sensitivity to antibiotics targeting the ETC and the accuracy of protein synthesis. Our results suggest that RbbA appears to function together with YhjD as part of a regulatory network that impacts bacterial oxidative phosphorylation and translation efficiency.
PMCID: PMC3083400  PMID: 21556145
9.  Chemical-genetic profile analysis of five inhibitory compounds in yeast 
BMC Chemical Biology  2010;10:6.
Chemical-genetic profiling of inhibitory compounds can lead to identification of their modes of action. These profiles can help elucidate the complex interactions between small bioactive compounds and the cell machinery, and explain putative gene function(s).
Colony size reduction was used to investigate the chemical-genetic profile of cycloheximide, 3-amino-1,2,4-triazole, paromomycin, streptomycin and neomycin in the yeast Saccharomyces cerevisiae. These compounds target the process of protein biosynthesis. More than 70,000 strains were analyzed from the array of gene deletion mutant yeast strains. As expected, the overall profiles of the tested compounds were similar, with deletions for genes involved in protein biosynthesis being the major category followed by metabolism. This implies that novel genes involved in protein biosynthesis could be identified from these profiles. Further investigations were carried out to assess the activity of three profiled genes in the process of protein biosynthesis using relative fitness of double mutants and other genetic assays.
Chemical-genetic profiles provide insight into the molecular mechanism(s) of the examined compounds by elucidating their potential primary and secondary cellular target sites. Our follow-up investigations into the activity of three profiled genes in the process of protein biosynthesis provided further evidence concerning the usefulness of chemical-genetic analyses for annotating gene functions. We termed these genes TAE2, TAE3 and TAE4 for translation associated elements 2-4.
PMCID: PMC2925817  PMID: 20691087
10.  Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins 
PLoS Biology  2009;7(4):e1000096.
One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans' biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.
Author Summary
One goal of modern biology is to chart groups of proteins that act together to perform biological processes via direct and indirect interactions. Such groupings are sometimes called functional modules. The types of protein interactions within modules include physical interactions that generate protein complexes and biochemical associations that make up metabolic pathways. We have combined proteomic and bioinformatic tools, and used them to decipher a large number of protein interactions, complexes, and functional modules with high confidence. In addition, exploring the topology of the resulting interaction networks, we successfully predicted specific biological roles for a number of proteins with previously unknown functions, and identified some potential drug targets. Although our work is focused on E. coli, our phylogenetic projections suggest that a considerable fraction of our observations and predictions can be extrapolated to many other bacterial taxa. As all the data derived from this study are publicly available, others may build on our work for further hypothesis-driven studies of gene function discovery.
A novel resource integrating proteomic and genome context-based tools provides a "systems-wide" functional blueprint ofE. coli, with insights into the biological and evolutionary significance of previously uncharacterized proteins.
PMCID: PMC2672614  PMID: 19402753
11.  Chemical-genetic profile analysis in yeast suggests that a previously uncharacterized open reading frame, YBR261C, affects protein synthesis 
BMC Genomics  2008;9:583.
Functional genomics has received considerable attention in the post-genomic era, as it aims to identify function(s) for different genes. One way to study gene function is to investigate the alterations in the responses of deletion mutants to different stimuli. Here we investigate the genetic profile of yeast non-essential gene deletion array (yGDA, ~4700 strains) for increased sensitivity to paromomycin, which targets the process of protein synthesis.
As expected, our analysis indicated that the majority of deletion strains (134) with increased sensitivity to paromomycin, are involved in protein biosynthesis. The remaining strains can be divided into smaller functional categories: metabolism (45), cellular component biogenesis and organization (28), DNA maintenance (21), transport (20), others (38) and unknown (39). These may represent minor cellular target sites (side-effects) for paromomycin. They may also represent novel links to protein synthesis. One of these strains carries a deletion for a previously uncharacterized ORF, YBR261C, that we term TAE1 for Translation Associated Element 1. Our focused follow-up experiments indicated that deletion of TAE1 alters the ribosomal profile of the mutant cells. Also, gene deletion strain for TAE1 has defects in both translation efficiency and fidelity. Miniaturized synthetic genetic array analysis further indicates that TAE1 genetically interacts with 16 ribosomal protein genes. Phenotypic suppression analysis using TAE1 overexpression also links TAE1 to protein synthesis.
We show that a previously uncharacterized ORF, YBR261C, affects the process of protein synthesis and reaffirm that large-scale genetic profile analysis can be a useful tool to study novel gene function(s).
PMCID: PMC2613417  PMID: 19055778
12.  Colony size measurement of the yeast gene deletion strains for functional genomics 
BMC Bioinformatics  2007;8:117.
Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences.
Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research.
GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations.
PMCID: PMC1854909  PMID: 17408490
13.  PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs 
BMC Bioinformatics  2006;7:365.
Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.
Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.
PIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.
PMCID: PMC1557541  PMID: 16872538
14.  Methylation of Histone H3 by Set2 in Saccharomyces cerevisiae Is Linked to Transcriptional Elongation by RNA Polymerase II 
Molecular and Cellular Biology  2003;23(12):4207-4218.
Set2 methylates Lys36 of histone H3. We show here that yeast Set2 copurifies with RNA polymerase II (RNAPII). Chromatin immunoprecipitation analyses demonstrated that Set2 and histone H3 Lys36 methylation are associated with the coding regions of several genes that were tested and correlate with active transcription. Both depend, as well, on the Paf1 elongation factor complex. The C terminus of Set2, which contains a WW domain, is also required for effective Lys36 methylation. Deletion of CTK1, encoding an RNAPII CTD kinase, prevents Lys36 methylation and Set2 recruitment, suggesting that methylation may be triggered by contact of the WW domain or C terminus of Set2 with Ser2-phosphorylated CTD. A set2 deletion results in slight sensitivity to 6-azauracil and much less β-galactosidase produced by a reporter plasmid, resulting from a defect in transcription. In synthetic genetic array (SGA) analysis, synthetic growth defects were obtained when a set2 deletion was combined with deletions of all five components of the Paf1 complex, the chromodomain elongation factor Chd1, the putative elongation factor Soh1, the Bre1 or Lge1 components of the histone H2B ubiquitination complex, or the histone H2A variant Htz1. SET2 also interacts genetically with components of the Set1 and Set3 complexes, suggesting that Set1, Set2, and Set3 similarly affect transcription by RNAPII.
PMCID: PMC427527  PMID: 12773564

Results 1-14 (14)